Global ETD Search

1	USING REINFORCEMENT LEARNING FOR ACTIVE SHOOTER MITIGATION Robert Eugen Bott (11791199) 20 December 2021 (has links) This dissertation investigates the value of deep reinforcement learning (DRL) within an agent-based model (ABM) of a large open-air venue. The intent is to reduce civilian casualties in an active shooting incident (ASI). There has been a steady increase of ASIs in the United States of America for over 20 years, and some of the most casualty-producing events have been in open spaces and open-air venues. More research should be conducted within the field to help discover policies that can mitigate the threat of a shooter in extremis. This study uses the concept of dynamic signage, controlled by a DRL policy, to guide civilians away from the threat and toward a safe exit in the modeled environment. It was found that a well-trained DRL policy can significantly reduce civilian casualties as compared to baseline scenarios. Further, the DRL policy can assist decision makers in determining how many signs to use in an environment and where to place them. Finally, research using DRL in the ASI space can yield systems and policies that will help reduce the impact of active shooters during an incident. Computer Software Autonomous Vehicles Simulation and Modelling Private Policing and Security Services Active shooter Active shooting incident Modeling and Simulation (M&S) Machine Learning reinforcement learning Homeland security AnyLogic Pathmind Mass casualty incidents mass casualty scenarios Outdoor Venues Open-air venues Open spaces active shooter mitigation uav shooter detection dynamic signage
2	Active Shooter Mitigation for Open-Air Venues Braiden M Frantz (8072417) 04 August 2021 (has links) <p>This dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.</p> Computer Software Adaptive Agents and Intelligent Robotics Computer Graphics Simulation and Modelling Computer-Human Interaction active shooter AnyLogic casualty rate casualty mitigation Gilroy first responder police patrolling drones reinforcement learning machine learning Pathmind security armed guard mass evacuation exit throughput dynamic signage emergency response shooter discharge rate movement speed agent logic mitigation gunshot triangulation shotspotter unmanned aerial systems

Search results

USING REINFORCEMENT LEARNING FOR ACTIVE SHOOTER MITIGATION

Active Shooter Mitigation for Open-Air Venues